"net"
	"syscall"

	"github.com/minio/minio/internal/color"
)

// Err is a structure which contains all information
// to print a fatal error message in json or pretty mode
// Err implements error so we can use it anywhere
type Err struct {
	msg    string
	detail string
	action string
	hint   string
}

// Clone returns a new Err struct with the same information
func (u Err) Clone() Err {
	return Err{
		msg:    u.msg,

   * @param queue a reference queue that the thread will poll.
   * @param frqReference a phantom reference to the FinalizableReferenceQueue, which will be queued
   *     either when the FinalizableReferenceQueue is no longer referenced anywhere, or when its
   *     close() method is called.
   */
  public static void startFinalizer(
      Class<?> finalizableReferenceClass,
      ReferenceQueue<Object> queue,
      PhantomReference<Object> frqReference) {

- Temporary credentials are short-term, as the name implies. They can be configured to last for anywhere from a few minutes to several hours. After the credentials expire, MinIO no longer recognizes them or allows any kind of access from API requests made with them.

Additionally, we often need to convert to various different client forms for different libraries we integrate with.

`kube.Client` just bundles these all in one place, so we have a single object to pass around anywhere we need Kubernetes access.
Additionally, it has a fake client variant, that does the same thing but with fake clients for use in unit tests.

  // gc'd even if there is a still a FinalizableReferenceQueue in a static field. (Setting the field
  // to null would also work, but only if there are no references to the FRQ anywhere else.)
  private WeakReference<ClassLoader> doTestUnloadableInStaticFieldIfClosed() throws Exception {
    final ClassLoader myLoader = getClass().getClassLoader();

or networks.

For performance reasons, the default TensorFlow server does not include any
authorization protocol and sends messages unencrypted. It accepts connections
from anywhere, and executes the graphs it is sent without performing any checks.
Therefore, if you run a `tf.train.Server` in your network, anybody with access
to the network can execute arbitrary code with the privileges of the user

But that is still not that useful.

Let's make it give us the current user.

## Create a user model

First, let's create a Pydantic user model.

The same way we use Pydantic to declare bodies, we can use it anywhere else:

//// tab | Python 3.10+

```Python hl_lines="5  12-16"
{!> ../../docs_src/security/tutorial002_an_py310.py!}
```

////

//// tab | Python 3.9+

```Python hl_lines="5  12-16"

* Generate <a href="https://json-schema.org" class="external-link" target="_blank">JSON Schema</a> definitions for your model, you can also use them anywhere else you like if it makes sense for your project.
* Those schemas will be part of the generated OpenAPI schema, and used by the automatic documentation <abbr title="User Interfaces">UIs</abbr>.

## Automatic docs

 *   <li>Convert in the "backward" direction using {@code converter.reverse().convert(b)} or {@code
 *       converter.reverse().convertAll(bs)}.
 *   <li>Use {@code converter} or {@code converter.reverse()} anywhere a {@link
 *       java.util.function.Function} is accepted (for example {@link java.util.stream.Stream#map
 *       Stream.map}).
 *   <li><b>Do not</b> call {@link #doForward} or {@link #doBackward} directly; these exist only to

But the client/user will not see it. Instead, the client will receive an "Internal Server Error" with an HTTP status code `500`.

It should be this way because if you have a Pydantic `ValidationError` in your *response* or anywhere in your code (not in the client's *request*), it's actually a bug in your code.

And while you fix it, your clients/users shouldn't have access to internal information about the error, as that could expose a security vulnerability.